In long term follow-up of cohorts of individuals, it is usual that the same set of physiological characteristics is measured at each of a number of specified points during the period of follow-up. Numerous examples may be cited from the literature on heart disease and cancer epidemiology and from the literature on clinical trials in these areas. If the losses of participants that inevitable occur in such studies are related to levels of the physiological variables the joint distribution of the variables over time is distorted. This distortion is, of course, relative to a cohort undisturbed by losses. This raises questions as to how one should define and estimate the expected time course of repeatedly measured variables; implicit in such a question are further questions of appropriate definition of the joint distribution of repeated measurements and estimation of parameters other than the vector of means. It appears essential in investigations of these issues to make parametric assumptions. In the proposed research we assume that an individual's set of observations at a discrete set of time points is an observation on a multivariate normal distribution and that any such set of observations may be probabilistically censored in the intervals between predesignated times of measurement and the conditional probability of removal in an interval is given by a probit model with independent variables being previously observed levels of variables. Taking the "loss-free" distribution of variate values as a basis for defining the time course of variables, problems for investigation include theoretical and numerical evaluation of how sever censoring must be to imply important distortion of the mean value vectors and other parameters of interest and study of the extent to which unbiased estimation of parameters is possible. The selection problem we have described above allows a multitude of specific formulations from fairly simple ones to ones of complexity greater than can be practically evaluated. The mathematical tools which we would use would be at an elementary analysis level. It is anticipated that the fact that a probit model is particularly suitable as a model for loss when risk variable data are mult-normal variates may allow fairly extensive analytic investigations but much simulation and numerical integration will be required. Research on Early Stopping in Clinical Trials may be extended and other topics suggested by on-going professional contacts may be initiated.